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Abstract:

Quantum vortices play a crucial role in both equilibrium and dynamical phenomena in two-dimensional (2D) superfluid systems. Experimental detection of these excitations in 2D ultracold atomic gases typically involves examining density depletions in absorption images, however the presence of a significant phononic background renders the problem challenging, beyond the capability of simple algorithms or the human eye. Here, we utilize a convolutional neural network to detect vortices in the presence of strong long- and intermediate-length scale density modulations in finite-temperature 2D Bose gases. We train the model on datasets obtained from ab initio Monte Carlo simulations using the classical-field method for density and phase fluctuations, and Gross–Pitaevskii simulation of realistic expansion dynamics. We use the model to analyze experimental images and benchmark its performance by comparing the results to the matter-wave interferometric detection of vortices, confirming the observed scaling of vortex density across the Berezinskii–Kosterlitz–Thouless critical point. The combination of a relevant simulation pipeline with machine-learning methods is a key development towards the comprehensive understanding of complex vortex-phonon dynamics in out-of-equilibrium 2D quantum systems.